Recently, there is a flurry of interest in closing the performance gap between generative models and discriminative models [1]}, [2]}, [3]}, [4]}. Among them, IGEBM [1]} and JEM [2]} are the two most representative ones, which reinterpret CNN classifiers as the energy-based models (EBMs) for image generation. Since the CNN classifier is the only trained model, which has a high compositionality, it is possible that a single trained CNN model may encompass the generative capabilities into the discriminative model without sacrificing its discriminative power. Their works realize the potential of EBMs in hybrid modeling and achieve improved performances on discriminative and generative tasks. Specifically, JEM [2]} reinterprets the standard softmax classifier as an EBM and achieves impressive performances in image classification and generation simultaneously, and ignites a series of follow-up works [8]}, [9]}, [3]}.
